A dynamic truck scheduling framework for open-pit mine production under equipment fault uncertainty

Open-pit mine production involves various types of equipment, and unexpected equipment failures can cause truck delays, idle shovels, and scheduling chaos. Traditional scheduling methods usually struggle to deal with these unexpected situations, impacting production continuity and efficiency. This paper proposes a dynamic truck scheduling framework to address the challenges of equipment failures in open-pit mine production systems, aiming to achieve production continuity and minimize the impact of failures. The framework consists of two phases: production scheduling and dynamic adjustment scheduling. In the production scheduling phase, a two-stage model is developed to optimize shovel-truck allocation and scheduling to generate a global scheduling plan. In the dynamic adjustment scheduling phase, based on real-time equipment status monitoring, a dynamic adjustment strategy and a dynamic adjustment model are proposed to enable the reassignment of transportation tasks. To solve the models efficiently, a joint heuristic framework that combines an improved multi-objective particle swarm optimization with a genetic algorithm. A case study of a coal mine in Inner Mongolia, China validates the proposed approach. Results show that, compared to traditional methods, the proposed approach significantly reduces fault response time by 55%, transportation cost by 12.6%, and truck waiting time by 30.4%. This paper provides theoretical guidance and technical support for open-pit mining operations to handle equipment failures and enhance the robustness of scheduling systems.